Melissa provides a lightweight, extensible architecture for building AI-driven agents without requiring extensive boilerplate code. At its core, the framework leverages a plugin-based system where developers can register custom actions, data connectors, and memory modules. The memory subsystem enables context preservation across interactions, enhancing conversational continuity. Integration adapters allow agents to fetch and process information from APIs, databases, or local files. By combining a straightforward API, CLI tools, and standardized interfaces, Melissa streamlines tasks such as automating customer inquiries, generating dynamic reports, or orchestrating multi-step workflows. The framework is language-agnostic for integration, making it suitable for Python-centric projects and can be deployed on Linux, macOS, or Docker environments.

Skeernir is an open-source AI agent framework designed to accelerate the development of puppet master agents for game automation and process orchestration. The project includes a base template, core APIs, and sample modules that demonstrate how to connect agent logic to target environments, whether simulating gameplay or controlling operating system tasks. Its extensible architecture allows users to implement custom decision-making strategies, plug in machine learning models, and manage agent lifecycles across Windows, Linux, and macOS. With built-in logging and configuration support, Skeernir streamlines testing, debugging, and deployment of autonomous AI agents.